Contrast ventriculography is a procedure that is routinely performed in clinical practice during cardiac catheterization. Catheters must be intravascularly inserted within the heart, for example, to measure cardiac volume and/or flow rate. Ventriculograms are X-ray images that graphically represent the inner (or endocardial) surface of the ventricular chamber. These images are typically used to determine tracings of the endocardial boundary at end diastole, when the heart is filled with blood, and at end systole, when the heart is at the end of a contraction during the cardiac cycle. By tracing the contour or boundary of the endocardial surface of the heart at these two extremes in the cardiac cycle, a physician can determine the size and function of the left ventricle and can diagnose certain abnormalities or defects in the heart.
To produce a ventriculogram, a radio opaque contrast fluid is injected into the left ventricle (LV) of a patient's heart. An X-ray source is aligned with the heart, producing a projected image representing, in silhouette, the endocardial surface of the heart (myocardium) muscle. The silhouette image of the LV is visible because of the contrast between the radio opaque fluid and other surrounding physiological structure. Manual delineation of the endocardial boundary is normally employed to determine the contour, but this procedure requires time and considerable training and experience to accomplish accurately. Alternatively, a medical practitioner can visually assess the ventriculogram image to estimate the endocardial contour, but such evaluation is often little better than an educated guess, particularly if the ventriculogram being assessed was made at or near end systole. Clearly, an automated border detection technique that can produce more accurate results, in much less time than the manual evaluation, would be preferred.
Several automatic border detection algorithms have been developed to address the above-noted problem. In U.S. Pat. No. 5,268,967, a number of different prior art methods are discussed for improving the definition with which images can be resolved to identify specific portions of the body. It is suggested by this reference that a histogram-based tone-scale transformation is a simple and effective way to adjust the contrast of an image, but that other techniques must be employed to distinguish the desired foreground portion of an image from the background clutter and to distinguish the object in question from the foreground and background. After discussing what others have done to achieve this goal and the problems with these techniques, the patent discloses a method that can be applied to any digital radiographic input image. The method disclosed in the patent includes the steps of edge detection, block generation, block classification, block refinement, and bit map generation. More specifically, after the edges of the object are detected in the first step, the image is broken into a set of nonoverlapping, contiguous blocks of pixels, which are classified into foreground, background, and object, on a block-by-block basis. The block classification step determines in which of ten possible states each block belongs, using a set of clinically and empirically determined decision rules. By evaluating the fine structure within each block, the block classification is refined, so that a two-valued or binary image is produced that functions as a template for any further image processing to be done on the image.
Another technique related to automated border detection is based upon identifying a gradient of the gray scale values comprising an image. In this prior art technique, a gray scale threshold gradient value is applied to process the gray scale image data of a ventriculogram in order to identify the boundary of the LV, and further processing may be employed to improve the accuracy with which the border is identified. Alternatively, it is suggested that landmarks or recognizable shapes or gray scale value combinations can be tracked over time to determine the direction and velocity of motion, which are represented as flow vectors. By analyzing the pattern of flow vectors, motion of the organ can be assessed. However, these flow vectors do not directly indicate the contour of the organ.
Yet another technique that is sometimes employed to determine the contour of an organ is based on digital subtraction. A mask image is recorded prior to introduction of a radio opaque contrast substance into the organ. This mask image may contain radio opaque structures such as ribs and vertebrae, which tend to interfere with discerning the contour of the organ. After the radio opaque contrast substance is introduced into the organ and a second image produced, the mask image is digitally subtracted from the second image, thereby removing the clutter in the second image that is not the organ in question. In practice, this technique is difficult to implement because registration between the mask image and the subsequent second image of the organ made perceptible by the radio opaque contrast substance is difficult to achieve. A variation of this technique employs time interval delay subtraction, wherein an image that was previously made close in time is subtracted from an image being analyzed, so that a difference image is produced that contains only the part of the organ that moved during the time interval between the two images. However, any part of the organ that does not move between the times that the two images were made cannot be delineated.
Morphological operators can also be employed to process image data in order to define boundaries of objects. Such techniques are often more general in application, e.g., relating to artificial vision systems, and are therefore not constrained by physiological considerations.
A paper entitled "Medical Image Analysis using Model-Based Optimization" by James S. Duncan, Lawrence H. Staib, Thomas Birkholzer, Randall Owen, P. Anandan, and Isil Bosma (IEEE, 1990), suggests the use of mathematical models based on empirically determined data for analysis of didgnostic medical images. In the second example discussed in this paper, a parametric shape model with an image-derived measure of boundary strength is employed. Use of the empirical data for the model improves its accuracy, but the results are still somewhat arbitrary.
Most of the prior art dealing with the problem of boundary determination has focused on analysis of a single image. Even the best of the currently available automated algorithms for determining the contour of an organ such as the heart have a very low success rate and typically require human correction to avoid significant errors. One of the greatest problems with existing automated techniques is that they do not apply knowledge of the expected shape and motion of the specific chamber/organ being analyzed in the manner that a physician would if evaluating the image. To attain at least the accuracy of an expert analyzing the image, an automated method should employ as much information derived from the imaging as possible to delineate the surface of the organ. Further, the automated system should accomplish the task more efficiently and quickly than a human. Toward that end, it has become apparent that more information than can be obtained from a single image will improve the accuracy with which an automated technique can determine the contour of an organ. Analysis of more than one image can provide the additional information needed for an automated method to achieve greater accuracy and can provide the physician more information about the heart or other organ than current techniques.